897 results about "Data Origin" patented technology

The invention provides an integrated sharing method and platform for health data of old people, and relates to the technical field of data processing.The method comprises the steps that multi-source heterogeneous health data of the old people are accessed, a rule template in a historical grading preprocessing rule base is matched based on data source capacity and data features, and data cleaning and standardization are completed; further identifying a sensitive field based on a medical data standard and an industry privacy specification, generating a sensitive field vector, carrying out sensitive level scoring, and calling a data desensitization strategy library to execute desensitization operations such as de-identification and masking; an access control function is constructed based on user attributes, access context and field sensitivity levels, authorization levels are determined, differential encryption is performed, and authorization views and data tokens are generated for secure access. Through the method, the problems of heterogeneous sources, complex processing, insufficient privacy protection and lack of flexibility of a sharing mechanism of old-age health data in the prior art can be solved.

The present invention relates to a system and method for cross-domain predictive modeling using Bedrock-based foundation models and blockchain-anchored data. The invention integrates large-scale foundation model reasoning with distributed ledger-based data provenance to enable verifiable, secure, and explainable predictive analytics across heterogeneous domains such as finance, healthcare, logistics, and environmental systems. The system comprises a data ingestion unit for receiving and normalizing multi-domain datasets, a blockchain anchoring unit for generating cryptographic hashes and recording data provenance into a distributed ledger, a cross-domain harmonization processor for aligning heterogeneous feature representations into a unified latent space, a foundation model processor configured to execute Bedrock-based predictive inference with adaptive domain contextualization, a verification processor for validating predictions against blockchain-anchored ground truths, and a governance processor for maintaining immutable audit trails of model evolution.

The invention relates to an internet big data extraction method and device, equipment and a storage medium, and the method comprises the following steps: carrying out distributed crawler collection on an internet data source, obtaining original network data, and converting the original network data into a structured data matrix; and performing multi-level semantic analysis on the matrix, constructing a semantic feature map, and performing topic segmentation and classification to form a topic domain knowledge tree. Association rules in the knowledge tree are further mined, and an implicit knowledge network is constructed. Performing semantic decomposition and expansion on query conditions based on the network to generate expanded query data, and performing similarity matching with the knowledge network to obtain a candidate data set; and finally, performing multi-factor sorting and extraction on the candidate data, and outputting target data, thereby solving the technical problem that in the second-hand car market, due to wide data sources and fuzzy semantics, an existing system has relatively large deviation when performing price prediction and maintenance cost analysis.

The invention relates to the technical field of industrial defect detection, and discloses an overflowing surface defect detection and identification method and system of a multi-modal feature and prompt mechanism, and the method comprises the steps: collecting image data and point cloud data of the surface of a water turbine runner overflowing surface, and converting the point cloud data into a surface defect two-dimensional image; obtaining a high-resolution defect image based on an image super-resolution reconstruction method of a deep recursive network; inputting a defect image into the generative adversarial network, judging a data source, generating a high-quality defect image through an objective function optimization generative model, and expanding a data set for a defect identification detection model to use; and inputting the point cloud data into the PDE-Net to construct a dynamic adjacency graph, and mapping the point cloud data to a specific defect category after graph convolution and multi-layer feature extraction. The method has the beneficial effects that a mixed attention mechanism is introduced in a multi-modal feature extraction stage, global information and local details in a defect image are fully extracted, and the detection capability of complex defects is improved.

The invention provides a multi-source data management method based on machine learning and a related device, and the method comprises the steps: obtaining a multi-source data set in an emergency command rescue scene, carrying out the data preprocessing of the multi-source data set, obtaining a standardized data set with a unified data form, extracting a data element information set from the standardized data set, and carrying out the processing of the data element information set. And calling a pre-trained blood relationship modeling model to perform association analysis processing on the data element information set, generating a blood relationship description set among the data entries in the standardized data set, and generating a data blood relationship tracking result based on the blood relationship description set. The data consanguinity tracking result comprises a historical record of the full life cycle of the data and cross-data entry dependency graph information. According to the invention, the method can provide a governance support with a data source capable of being checked, a processing process capable of being traced and a clear dependency relationship for emergency command rescue, enables a rescue decision to be developed based on reliable data consanguinity information, and effectively improves the support capability of data governance for the rescue decision.

The invention discloses an intelligent safety inspection system for a construction site, and relates to the technical field of construction inspection, multiple data sources of the construction site are obtained through a data acquisition module, and a data fusion and processing module dynamically optimizes heterogeneous data through time synchronization, format standardization and noise filtering in combination with a self-adaptive weighted fusion algorithm, so that the construction site safety is improved. The intelligent analysis and early warning module evaluates the data difference degree based on machine learning, adopts clustering analysis to mine an abnormal mode for high-difference data, carries out safety risk identification on low-difference data, and dynamically adjusts an early warning strategy. The terminal interaction module pushes the inspection result and the early warning information to the mobile terminal APP, the monitoring center display screen and the voice broadcast device, so that a manager can remotely check the construction condition and quickly make a decision, the intelligent level of construction site safety inspection can be remarkably improved, efficient and accurate potential safety hazard recognition and dynamic early warning are achieved, and the safety inspection efficiency is improved. And the safety accident risk is effectively reduced.

The invention discloses an intelligent physical examination data quality control analysis method and system, and relates to the technical field of medical quality control. The problems that existing physical examination data are diversified in source, complex in format, single in quality control means and the like are solved. Structured data, medical images and texts are integrated, and standardized processing and data mapping are achieved through a unified platform. Static and dynamic rule libraries are constructed, and the threshold is dynamically adjusted in combination with individual features, so that personalized risk assessment is realized. Structured data anomaly detection is performed by adopting machine learning, an image quality problem is identified by utilizing a convolutional neural network, and text anomaly is processed and analyzed through a natural language, so that the anomaly detection accuracy is improved. The system realizes multi-dimensional quality control and intelligent early warning, enhances the integrity and credibility of physical examination data, supports precise health management and disease early warning, improves the quality control efficiency, meets the intelligent analysis requirements of large-scale multi-modal physical examination data, and promotes the application of intelligent health management.

The invention belongs to the technical field of computing resource scheduling, and particularly relates to an intelligent scheduling method and system for Huaan Atlas heterogeneous computing resources based on dynamic load awareness. The method comprises the steps that the real-time state of multi-dimensional hardware data is collected, and a basic data source is provided for subsequent steps; dynamically adapting tasks and hardware characteristics through a matching degree matrix, modeling aiming at various basic data, and constructing a state vector required by reinforcement learning; predicting a fault risk score through a lightweight prediction model deployed at each computing node; and a deep Q network is adopted as a model architecture, a state vector and a fault risk score are input, reinforcement learning training is performed through a reward function in a multi-target vector form, a final scheduling model is obtained, and a task allocation decision is output. The problems that in the prior art, the hardware state cannot be sensed in real time, hardware characteristic matching is ignored, consequently, the computing resource utilization rate is insufficient, and fault recovery is passive are solved.

The invention discloses a mechanical equipment multi-state selective maintenance method based on deep learning, and relates to the technical field of mechanical equipment intelligent prediction maintenance, and the method comprises the following steps: deploying a sensor at mechanical equipment to collect operation state data in real time, collecting maintenance operation logs and historical fault diagnosis record text data, and storing the collected maintenance operation logs and historical fault diagnosis record text data; generating a multi-source data stream through timestamp alignment, and preprocessing the multi-source data stream to obtain a preprocessed multi-modal data set; according to the method, the multi-modal data collected for the mechanical equipment has comprehensiveness and availability of data sources, the utilization rate of heterogeneous data is improved, the multi-modal feature data is fused by using the cross-modal association mapping model, hidden information can be mined, the invisible state can be recognized, the equipment operation condition is clearly mastered, and the method is suitable for popularization and application. And through combination of the STCN and the Weibull-deep learning hybrid model, the residual life of the mechanical equipment can be dynamically and accurately predicted.

The invention relates to the technical field of semantic label generation management, in particular to an enterprise-level customer data dynamic label management system which comprises an enterprise customer data storage unit, a knowledge fusion unit, a semantic analysis unit and a label generation application unit. The enterprise customer data storage unit obtains enterprise customer data and product related data and broadens data sources, the knowledge fusion unit carries out data standardization processing, entities and relationships are extracted by utilizing an advanced technology and are stored in a knowledge graph, and the semantic analysis unit selects samples by adopting stratified sampling and active learning, so that the data source is expanded. A plurality of technical means are combined to judge and process semantic drift, a deep learning model is optimized, a label generation application unit generates dynamic labels according to an optimization model, the dynamic labels are updated to an enterprise customer relationship management center in real time after clustering, screening and matching operation, real intentions of customers can be accurately obtained, accurate semantic labels can be generated, and the real intentions of the customers can be accurately analyzed. Enterprises are assisted to accurately serve customers, and the market competitiveness is improved.

The invention discloses an automatic financial data collection system based on a web crawler technology. The system comprises an intelligent crawler collection module, a financial data processing module and a data collection module. The intelligent crawler acquisition module comprises a dynamic scheduling crawler cluster unit, a multi-format capture engine and a metadata marking unit; the dynamic scheduling crawler cluster configures a routing algorithm based on reinforcement learning, and dynamically adjusts an IP proxy strategy through an anti-crawling feature matrix; the multi-format capture engine is integrated with a PDF / HTML / JSON heterogeneous data analysis channel; the metadata marking unit generates a data source label by adopting multi-layer coding conversion; and the financial data processing module comprises a cross-format analyzer, a data cleaning pipeline and a structured storage unit. According to the scheme, through innovative designs such as multi-modal data analysis, self-adaptive anti-creeping confrontation, dynamic resource scheduling and intelligent association analysis, the core problems of low heterogeneous data integration efficiency, weak dynamic anti-creeping confrontation capability and the like are solved.

The invention discloses a trusted data space privacy protection method and system based on zero knowledge proof, and belongs to the technical field of data processing.The method comprises the steps that real-time operation data of multiple data sources are collected through the Internet of Things, and a real-time operation data set and a data source information set are obtained; on the basis of the data source information set, zero-knowledge certification is conducted on the real-time operation data set according to real-time certification complexity, a certification data set is generated, and the real-time certification complexity is comprehensively analyzed and determined on the basis of safety perception information, the data privacy degree, the data anomaly degree and the data fluctuation degree; and carrying out encrypted storage on the proof data set by utilizing a block chain technology, and carrying out data sharing and data verification according to the encrypted storage data set. The technical problems that in the prior art, a dynamic privacy protection mechanism for multi-source data in the sharing process is lacked, so that sensitive data is prone to leakage, and credible verification is difficult to achieve are solved.

Systems and methods for tokenizing entities within a distributed ledger technology (DLT) network. A processing system operating in the DLT network receives an input dataset that includes asset attributes, identity information, or event metadata. The processing system encrypts the input dataset, partitions the encrypted data into multiple encrypted segments, and distributes each segment to selected decentralized storage nodes. Each encrypted segment is associated with a unique segment identifier. The processing system generates a cryptographic token directly linked to the input dataset and embeds metadata into the cryptographic token referencing encrypted segments, a data provenance identifier, or a creation timestamp. A smart contract deployed on the DLT network mints the cryptographic token and cryptographically links the embedded metadata to encrypted segments stored at decentralized storage nodes. The processing system, through the smart contract, provides authorized access to the input dataset according to access permissions defined within the smart contract.

The invention relates to an electricity market information extracting and pushing method and system based on multi-modal semantic fusion. The method comprises the steps that an electricity market field pre-training corpus is constructed; adopting a staged pre-training strategy; performing multi-modal coding on the input power market information data; a text confidence score and an image confidence score are generated, a cross-modal attention incidence matrix is established, and image-text fine-grained interaction features are captured and fused to generate unified semantic representation; a user model containing long-term stable preferences and short-term dynamic interests is established based on unified semantic representation generated through fusion, a sequence decreasing exploration strategy is adopted, high-correlation intelligence is pushed through known preferences, new domain content is randomly inserted to mine potential interests, and the weight of pushed content is adjusted in real time according to user feedback. The problems of heterogeneous sources, diversified formats, different standards, asymmetric information and the like of power market information data can be effectively solved, and accurate and real-time power market information pushing service is provided for users.

The invention discloses a data security sharing method and device for verifiable bilateral access control based on an attribute threshold, computer equipment and a storable medium, and aims to realize data privacy protection and source authenticity guarantee in an open cloud-edge collaborative environment. According to the method, an attribute threshold matching mechanism is introduced, a bilateral access control strategy is designed, decryption can be performed only when intersection of attribute sets of a data sender and a data receiver and access attribute sets specified by each other meets a preset threshold, and access authorization under fuzzy matching is supported. In order to ensure the authenticity and non-repudiation of the data, the attribute signature of the sender is embedded into the ciphertext structure, and the credibility of the data source is ensured. According to the method, a constant-size ciphertext structure is adopted, and an offline / online double-stage encryption technology is combined, so that the encryption delay and the calculation burden are reduced. At a decryption end, data verification and partial decryption tasks are outsourced to edge nodes, and a result verification mechanism is introduced, so that malicious nodes are effectively prevented from returning wrong results.

The invention discloses a data analysis visualization processing method and device based on index management and a server, and belongs to the technical field of data processing, and the method comprises the steps: extracting related original data from main data and business data storage positions according to the data source information of each index in a predefined index dictionary, processing the extracted original data into standardized data; for the standardized data, calculating each index value according to a predefined calculation formula in an index dictionary; and obtaining visual configuration content operated by a user, automatically generating a report containing a visual chart according to the calculated index values according to the visual configuration content, and receiving a user operation instruction to export the report of the visual chart. According to the invention, the enterprise data analysis efficiency is improved, the labor cost is saved, the data visualization operation is realized, and convenience is provided for the use of the user.

The invention discloses a charging data auditing method based on the Internet of Things. According to the invention, a multi-dimensional tamper-proof system is constructed through deep fusion of physical signal features and a data security technology. According to the dynamic data fingerprint technology, charging waveform microscopic characteristics and equipment space-time attributes are subjected to nonlinear coupling, so that data generated in each charging period has a unique and verifiable biological-grade identifier. The introduction of a chaotic mapping mechanism endows fingerprints with sensitivity to data subtle differences, even if an attacker tampers current and voltage values with the last three decimal points, an avalanche effect of fingerprint hash values can be caused, it is ensured that tampering behaviors are inevitably exposed in an auditing link, a full-link credible closed loop from data generation to auditing conclusions is achieved, and the auditing efficiency is improved. The reliability of data sources in auditing is improved, and the overall auditing accuracy is ensured. And through an innovative architecture of edge end lightweight calculation and cloud collaborative verification, the feasibility and economy of large-scale charging facility auditing are remarkably improved.

The invention belongs to the technical field of civil aviation automatic driving safety detection, and discloses a civil aviation airport apron airplane vehicle collision detection and prevention method and system. The method comprises the steps of super-large geographical range data fusion, target path prediction, target path tracking, danger approach prompt, smoothing of business data, collision detection of aircraft and vehicle operation and avoidance operation in various modes according to priority under the condition of full evaluation of delay and jitter of various data sources. The method breaks through the traditional limitation, formulates a scientific evaluation standard, optimizes the complex data processing capability, balances the real-time performance and accuracy, avoids the reduction of the guarantee capability caused by the traditional simple waiting flight, provides the precise data to support the civil aviation operation for the airport with the busy airport apron operation, provides the higher-efficiency guarantee support, and improves the safety of the airport. And the decision-making efficiency and the operation management level are obviously improved.

The invention discloses a trust evaluation data storage tracing method and system based on a block chain technology, and particularly relates to the technical field of data storage management, and the method comprises the following steps: recording trust evaluation data in a block form based on the distributed storage characteristics of a block chain, automatic management of data submission, verification, storage and updating is realized by using an intelligent contract, data authenticity and non-tampering are ensured, time stamps are added for data through a timestamp function, a time-ordered operation chain is formed, a data tracing function is provided, an authorized user is allowed to inquire a data source and historical operation records, and the user experience is improved. Based on data access characteristics, a storage area is dynamically adjusted, and efficient utilization and optimal management of storage resources are achieved; according to the method, the storage position of data can be dynamically adjusted, hot data are stored in a high-performance storage area, cold data are migrated to a low-cost storage area, the access efficiency of high-frequency data is guaranteed, and the storage cost is optimized.

The invention relates to the technical field of information, and discloses a digital archive classification query method based on large model index recognition, and the method comprises the steps: generating unified feature representation through the collection and processing of multi-modal data; optimizing information expression of the multi-modal data based on a feature fusion method; the consistency of data features and the accuracy of classification results are improved by utilizing a dynamic verification mechanism; the optimized features are embedded into a low-dimensional semantic space through a dimension reduction method, and classification tags are generated; and constructing a vector index supporting query and feedback optimization, wherein the collection of the multi-modal data comprises the following steps: collecting text data and collecting image data. Feature extraction is carried out on image data through the convolutional neural network, unified feature representation of texts, images, audios and structured numerical data is realized through collection and processing of multi-modal data, and the problems of single data source and inconsistent feature representation in the prior art are effectively solved.

The invention relates to the field of power system data analysis, and discloses a data integration and storage method and system for new energy grid-connected evaluation, and the method comprises the steps: collecting real-time data and key information, carrying out the classification and preprocessing of the collected data, and building a data synchronization and updating mechanism; carrying out calibration and consistency processing on the preprocessed data, carrying out data fusion through a connection algorithm, and verifying a fusion effect; and aligning and connecting the data through equipment ID and grid-connected point information, constructing logical association based on a power grid topological relation, and performing deep fusion and storage on new energy output data in a region and power grid operation data in combination with a clustering algorithm so as to realize data integration and dynamic evaluation. The method aims at solving the problems that data sources of new energy stations and power grid equipment are diverse, integration efficiency is low and grid-connected evaluation is difficult to support efficiently, the data integration process is optimized, grid-connected capacity evaluation precision and efficiency are improved, and the method is suitable for power system planning and management.

The invention provides an enterprise distribution information management method for migration-in and migration-out of an industrial platform, and relates to the technical field of data processing, and the method comprises the steps: carrying out formatting standard processing on a multi-source basic data set, unifying the field structure of each data source, removing structural conflict fields, and carrying out duplicate removal operation based on a data timestamp, obtaining an integrated data set; extracting an enterprise migration time field, an enterprise position field and an enterprise category field one by one; performing time segmentation on the data in the integrated data set to generate a periodic migration data set; according to the periodic migration data set and the enterprise position field, mapping the original position and the target position to a standard coordinate system, and generating a region migration distribution map; classifying and aggregating the enterprise migration behaviors according to the enterprise categories, evaluating the migration activeness of different categories of enterprises in a target area in a specific period, and generating a classification trend index of the enterprise migration-in and migration-out behaviors; according to the invention, autonomy and accuracy of enterprise distribution information management are improved.

The invention discloses an industrial building business service management method and device and a medium, and belongs to the technical field of building service management, and the method specifically comprises the steps: constructing a multi-dimensional data fusion model; defining cost, progress, quality and security dimension data ranges and acquisition nodes, and storing the data ranges and the acquisition nodes in a model database after standardization; based on the standardized data of the multi-dimensional data fusion model, analyzing a dimension internal relationship by using an association rule mining algorithm, defining a dimension mapping relationship, and integrating to form an association rule base; establishing a multi-dimensional linkage analysis module; the module is established by taking a model database as a data source and a multi-dimensional data association rule base as a logic support, and a threshold triggering mechanism and a cross-link data calling function are set; constructing a business collaborative decision support module; business requirements of multiple departments are integrated; a multi-dimensional data visualization monitoring platform is built; and designing a linkage trend chart based on a result of the multi-dimensional linkage analysis module.

The invention belongs to the technical field of knowledge Token generation and traceability, and discloses a knowledge Token generation and traceability method based on structured metadata, which is characterized in that a current knowledge scene is quickly matched and a core field is dynamically adjusted through few-sample learning in combination with various technical models and a preset industry field model library; a mixed semantic segmentation mode is adopted, knowledge fragments are completely segmented according to business logic, then a refined element extraction strategy is matched, three types of key elements including entities, relationships and clauses are accurately extracted, and errors are reduced through multi-dimensional verification; originally dispersed PDF, audio and video and other unstructured knowledge can be converted into standardized assets with anchoring information. Meanwhile, a multi-dimensional verification process is designed, comprehensive verification is carried out from a data source, access permission and logic consistency to a health state, it is ensured that a knowledge source can be checked, authenticity can be distinguished, and the information distortion risk during large model calling is reduced.

The invention provides a time sequence data storage method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining time sequence data and metadata information of the time sequence data, the metadata information comprising a first point location identifier, a data source and a data type corresponding to the time sequence data; according to the data source and the data type, determining a storage strategy of the time series data, including a target storage period and a target storage position; according to the storage strategy and the first point position identification, a target storage instance corresponding to the time sequence data is matched from a storage instance set, all storage instances in the storage instance set are combined and constructed according to different storage strategies and the point position identification, and all the storage instances are parallel storage instances; and storing the time sequence data to a time sequence database through the target storage instance. By determining a dynamic storage strategy driven by metadata and a parallel storage instance, the problem of performance bottleneck during high-concurrency writing of massive time series data is solved, and the real-time performance and reliability of time series data storage are ensured.

The invention discloses a dam seepage intelligent monitoring and risk early warning method, system and device based on an expert large model and a storage medium, and belongs to the field of hydraulic engineering monitoring, and the method comprises the steps: collecting multi-source heterogeneous data from a dam seepage monitoring system, and marking structural domain labels according to data sources and structural attributes; executing corresponding feature fusion processing on different types of data according to the structural domain labels, and constructing a structured feature vector group in a unified format; inputting the feature vector group into a structure-driven expert large model, activating a bound expert path according to a structural domain label, and executing multi-task reasoning; the multi-task reasoning result is input into a dam seepage virtual twin system, a simulation module is driven to conduct seepage diffusion evolution simulation, and a space risk map is generated; and generating an early warning signal and an emergency plan text according to the risk map and the multi-task reasoning result.

The invention discloses a traditional Chinese medicine intelligent diagnosis and treatment and health management system based on big data, and relates to the technical field of traditional Chinese medicine informatization, and the system comprises a multi-source data collection and integration unit, a big data analysis and mining unit, and an intelligent diagnosis and treatment and health management service unit. The built-in functions of the multi-source data acquisition and integration unit comprise data sources and data integration, the data sources comprise clinical medical record data, health monitoring data, ancient medical registration literature data and modern medical examination data, and the data integration comprises data cleaning, data standardization and data fusion; the built-in functions of the big data analysis and mining unit comprise disease and syndrome type correlation analysis, prescription curative effect evaluation and optimization, personalized health management analysis and medicine research and development auxiliary analysis, and the operation steps of the disease and syndrome type correlation analysis comprise feature extraction, machine learning modeling and model evaluation and optimization.

The invention discloses a financial product intelligent matching system based on multi-source data integration, and relates to the technical field of finance, and the system comprises a data access module which is used for collecting enterprise heterogeneous data from an industrial end and a multi-source system; the data cleaning and structuring module is used for cleaning, integrating and normalizing the heterogeneous data and generating enterprise label data according to a data system; the rule configuration module is used for configuring and maintaining an access rule based on the financial product and converting the access rule into a computable label; the intelligent matching module is used for realizing bidirectional matching by calculating the similarity between an enterprise and a product based on an enterprise label and a product access rule, and the business opportunity management and collaboration module is used for carrying out business opportunity scoring and level-to-level management on a matching result. According to the application, various data systems such as ERP, tax affairs, credit investigation, supply chains and the like are seamlessly connected, and integration and real-time acquisition of heterogeneous data such as enterprise operation data, credit rating, cash flow, asset information and the like at an industrial end are realized, so that data sources are more comprehensive and timely.

The invention discloses a power distribution network intelligent early warning and section positioning method based on transient recording data, and the method comprises the steps: carrying out the fault type recognition through the comprehensive analysis of the transient recording data, combining with an advanced algorithm, such as a relevance vector machine (RVM), carrying out the line abnormality recognition through a deep neural network, and accurately extracting the feature information of fault data, the limitation of an existing power distribution automation master station fault studying and judging method is effectively overcome, the problem of inaccurate fault studying and judging caused by the data problem of the intelligent terminal is avoided, the accuracy of fault studying and judging of the power distribution automation master station is integrally improved to a relatively high level, a reliable basis can be provided for fault processing, and the working efficiency is improved. Multi-source data such as a wave recording file, a topological structure and remote signaling and telemetering are fully fused, the operation state of the power distribution network is analyzed from multiple angles, compared with an analysis method of a single data source, the actual situation of the power distribution network can be reflected more comprehensively and accurately, and the reliability and accuracy of fault diagnosis and positioning are improved.

The invention relates to a real-time process monitoring method based on data analysis, and the method comprises the following steps: S1, building a three-dimensional component priority evaluation model based on a component operation scene type, a real-time resource occupancy rate and a data dependence degree, and dynamically matching an acquisition strategy; s2, a cleaning rule is adapted according to a data source, a feature extraction dimension is adjusted in combination with process dynamic features, and an improved time sequence decomposition algorithm is used for separating data trends, fluctuations and abnormal residual errors; s3, constructing an exclusive baseline sub-model by using an online learning algorithm according to a scene label, and establishing a scene switching mechanism; s4, in combination with component interaction anomaly features, an anomaly level is judged through a mixed detection model; s5, on the basis of exception processing and user feedback, constructing an error correction model optimization parameter; and S6, generating a report containing an abnormal propagation path, and triggering hierarchical collaborative response of the associated component. The invention aims to solve the problems of single acquisition dimension, no scene adaptability in preprocessing, incomplete abnormal detection and the like in the existing monitoring technology.